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§ Agent · BambooHR

The BambooHR data agent that acts the way you would.

It watches your BambooHR data alongside your finance and ops systems, on a schedule you set or whenever fresh data lands. When something needs attention, it tells you, or handles it the way you would.

D
DefiniteAPP9:14 AM · #ops-alerts
⚠️ Attrition in Engineering up 3.2x this quarter, 6 exits vs. 2-per-quarter baseline

Engineering voluntary terminations hit 6 this quarter against a trailing four-quarter average of 1.9. Four of the six were mid-level ICs with 18-24 months tenure, all in the same division. Backfill pipeline currently shows 2 open reqs.

Review & approve Dismiss
BambooHR Employment Status Change + Job Assignment + Organization Unit · cross-referenced to headcount plan · audit log

How an agent works

An agent watches one thing and acts on it. Not a workflow, just a standing watch that usually does nothing and acts the moment it should.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

HeadcountACROSS YOUR SOURCES

Reconcile your HRIS headcount to the plan and the budget

It reconciles your BambooHR employee directory and org structure against your headcount plan and finance data, and flags the gaps before the board meeting, so the headcount number on the ops deck matches the people actually on payroll.

EmployeeOrganization Unit
Attrition

Catch attrition spikes before they compound

When voluntary terminations break their trend in a department or tenure band, it tells you which teams are affected and how deep the bench gap is, looks at the job history for common patterns, and queues the escalation for you to approve.

Employment Status ChangeJob Assignment
Coverage

Flag PTO coverage gaps before they hit

When overlapping time-off requests would leave a team below operating threshold, it surfaces the dates, the roles affected, and the coverage risk so you can adjust before the week arrives.

Time OffEmployee
Custom

Run any Python it needs to get the job done

Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, kick off a job, reshape the data, or wire into your own tooling. The action space is yours to define.

Why not just build it yourself?

You could rig one of these with a cron job and a Slack webhook in an afternoon. The watching is the easy part. Here's what you'd own forever, and don't, here:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every BambooHR object, modeled and query-ready the moment you connect.

Employee
customermarketing
Organization Unit
customermarketing
Employment Status Change
infrastructure_devops
Job Assignment
customermarketing
Time Off
revenue_financeengagement
Employee Asset
supportengagement

It runs on your real BambooHR account (mid-cycle terminations, retroactive status changes, org restructures and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

Expose it to your own agents and tools over MCP, and drive it from your stack.

Run it in your own VPC or fully self-hosted. Everything it does is pure SQL and Python you can inspect.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

The watchers and actors. Once you've built one, it runs on its own, keeping an eye on what matters and acting the way you would.

Autonomous agents →

Get started

  1. 1Connect BambooHR, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh BambooHR data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.
Those report on BambooHR in isolation, when you ask. This watches continuously, reasons across BambooHR plus your finance and planning tools, and hands off to Fi to investigate why, so you find out before the ops review, not during it.

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